Open AccessA Hybrid Approach of Fuzzy C-means Clustering and Neural network to make Energy-Efficient heterogeneous Wireless Sensor network
Author(s) -
Amit Kaushik
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i2.pp674-681
Subject(s) - computer science , wireless sensor network , cluster analysis , adaptability , artificial neural network , fuzzy logic , efficient energy use , node (physics) , computer network , wireless network , energy (signal processing) , distributed computing , data mining , wireless , artificial intelligence , telecommunications , engineering , mathematics , ecology , statistics , structural engineering , electrical engineering , biology
The Wireless sensor network has been highly focused research area in recent times due to its wide applications and adaptability to different environments. The energy-constrained sensor nodes are always under consideration to increase their lifetime. In this paper we have used the advantages of two approaches i.e. fuzzy c-means clustering and neural network to make an energy efficient network by prolonging the lifetime of network. The cluster formation is done using FCM to form equally sized clusters in network and the decision of choosing cluster head is done using neural network having input distance from basestation, heterogeneity and energy of the node. Our Approach has successfully increased the lifetime and data capacity of the network and outperformed different approaches applied to the network present in literature.